import json
import pandas as pd
from datetime import datetime, timedelta
import match

import logging

from utils.verifyParameters import verify

# 配置日志
logging.basicConfig(level=logging.INFO)

def get_closest_sheet(file_path, station_config, recon_end_time):
    excel_file = pd.ExcelFile(file_path)
    sheet_names = excel_file.sheet_names
    closest_sheet = None
    min_diff = None
    closest_sheet_name = None
    for sheet in sheet_names:
        header_row = None
        df_tmp = pd.read_excel(file_path, sheet_name=sheet)
        if all(item in df_tmp.columns.tolist() for item in list(station_config["columns"].values())):
            header_row = -1
        else:
            for row in df_tmp.head(13).itertuples():
                if all(item in list(row[1:]) for item in list(station_config["columns"].values())):
                    header_row = row.Index
                    break
        if header_row is not None:
            df = pd.read_excel(file_path, sheet_name=sheet, skiprows=header_row + 1)
            try:
                df[station_config["columns"]["create_time"]] = pd.to_datetime(
                    df[station_config["columns"]["create_time"]], errors="coerce"
                )
                df = df.dropna(subset=[station_config["columns"]["create_time"]])
                df = df.dropna(subset=[station_config["columns"]["gas_num"]])
                latest_time = df[station_config["columns"]["create_time"]].max()
                if pd.notnull(latest_time):
                    time_diff = abs(
                        (latest_time.date() - datetime.strptime(recon_end_time, "%Y-%m-%d %H:%M:%S").date()).days
                    )
                    if min_diff is None or time_diff < min_diff:
                        min_diff = time_diff
                        closest_sheet = df
                        closest_sheet_name = sheet
            except Exception as e:
                logging.error(f"处理{sheet}时出错: {e}")
    return closest_sheet, closest_sheet_name



def process_chendushuangrui_reconciliation(recon_start_time, recon_end_time, file_path, fault_tolerant, station_id, ignore_time):
    """
        处理成都双瑞站对账单的特殊逻辑

        Args:
            recon_start_time (str): 对账开始时间，格式为 "YYYY-MM-DD HH:MM:SS"
            recon_end_time (str): 对账结束时间，格式为 "YYYY-MM-DD HH:MM:SS"
            file_path (str): 上传的Excel文件路径

        Returns:
            dict: 处理结果的JSON数据
        """
    try:
        # region 配置
        station_config = {
            "name": "成都双瑞",
            "ids": [1012],
            "main_body_gas_station": 153,
            "file_keyword": "(.*)成都双瑞(.*)",
            "columns": {
                "create_time": "加注时间",
                "car_number": "企业车牌",
                "gas_num": "加注数量",
                "gas_price": "结算价",
            },
            "diff_num": 1,  # 差异在x公斤以内的设置为疑似匹配
        }

        verify(fault_tolerant, station_id, ignore_time, station_config)

        # 处理时间范围
        start_time = datetime.strptime(recon_start_time, "%Y-%m-%d %H:%M:%S")
        start_time = (start_time - timedelta(hours=1)).strftime("%Y-%m-%d %H:%M:%S")
        end_time = datetime.strptime(recon_end_time, "%Y-%m-%d %H:%M:%S")
        end_time = (end_time + timedelta(hours=1)).strftime("%Y-%m-%d %H:%M:%S")

        closest_sheet, closest_sheet_name = get_closest_sheet(file_path, station_config, recon_end_time)

        try:

            station_data = closest_sheet[list(station_config["columns"].values())]

            # 对单价列进行四舍五入保留两位小数
            gas_price_col = station_config["columns"]["gas_price"]
            if gas_price_col in station_data.columns:
                station_data[gas_price_col] = station_data[gas_price_col].round(2)

            station_data = match.set_station_id_column(station_data, station_config)


        except Exception as e:
            logging.error('配置文件与excel不一致')
            raise RuntimeError('配置文件与excel不一致') from e
        logging.info(f"{len(station_data)} {closest_sheet_name}")
        station_dfs_dict = {}

        # step 2 处理Excel 如果是多个气站并且Excel中的字段配置包含station_name，就需要分多个气站进行对比。
        if "station_name" in station_config["columns"] and len(station_config["ids"]) > 1 and "nms" in station_config:
            station_names = station_config["nms"]
            station_name_col = station_config["columns"]["station_name"]
            for idx, pattern in enumerate(station_names):
                matched_df = station_data[station_data[station_name_col].astype(str).str.match(pattern, na=False)]
                # 如果有详细时间，需要对excel数据按照详细时间进行排序
                non_midnight_count = (
                        matched_df[station_config["columns"]["create_time"]].dt.strftime("%H:%M:%S") != "00:00:00"
                ).sum()
                if len(matched_df) > 0 and (non_midnight_count / len(matched_df)) > 0.5:
                    # 超过50%即为"绝大多数"
                    matched_df = matched_df.sort_values(by=station_config["columns"]["create_time"])
                station_dfs_dict[station_config["ids"][idx]] = matched_df

        match_result = []

        # step 3 如果有station_dfs_dict就循环这个，然后每个站点单独获取数据并对比，否则就获取配置中的所有站点数据并进行对比
        if len(station_dfs_dict) > 0:
            for i in station_dfs_dict:
                online_data = match.get_online_data([i], start_time, end_time)
                # 开始对比
                match_result.append(match.match_data_v1(station_dfs_dict[i], online_data, station_config))
        else:
            # excel获取时间区间内的数据
            if station_config.get("excel_get_time_range", False):
                station_data = station_data[
                    station_data[station_config["columns"]["create_time"]].between(start_time, end_time)]
            online_data = match.get_online_data(station_config['ids'], start_time, end_time)
            # 开始对比
            match_result.append(match.match_data_v1(station_data, online_data, station_config))

        # 生成JSON结果
        json_result = match.create_json_result_v2(match_result, station_config, start_time, end_time)
        return json_result

    except Exception as e:
        raise Exception(f"处理成都简阳对账单时出错: {str(e)}")
